The availability of accurate and up to date data regarding atmospheric conditions is of great importance to aircraft. In particular, it is of paramount importance to aircraft flying within terminal maneuvering areas, i.e. the areas around airports where safe passage to and from runways must be guaranteed in ever increasingly difficult conditions associated with highly dense convergences of maneuvering traffic.
Existing solutions for predicting wind and other atmospheric properties within a given airspace and time period are derived from meteorological models, traditionally developed by meteorologists. Such models typically rely on obtaining a computational solution of a more or less sophisticated computational fluid dynamics formulation of the atmospheric physics. An alternative approach relies on exploiting large amounts of observed atmospheric data to fit some sort of mathematical or statistical model to account for the atmospheric physics. As computing power grows, research efforts have shifted to approaches that rely on solid, physically-founded formulations such as those used in computational fluid dynamics, but that can incorporate real observations collected as the evolving atmospheric conditions are observed.
Existing meteorological models can be classified into several categories, depending on their spatial and temporal performance, their physical formulation and the type of outputs provided. Global models are very autonomous, working from coarse weather information updates provided by international meteorological agencies at high frequencies. Their granularity and accuracy make them useful for strategic planning of air vehicle trajectories (e.g. wind-optimal routes over oceanic regions). Mesoscale models go further and implement complex physical models to provide forecasts (for example, the forecasts typically used in public weather information services). These mesoscale models accept the output of global models, as well as in-situ measurements, as part of the input dataset. Local models use downscaling techniques to couple current and forecasted weather information with precise terrain models and other statistical databases. This achieves better estimations of atmospheric properties over relatively small geographical areas, but usually at the expense of significantly higher computational power and data requirements.
Although aviation is a major customer of meteorological forecasts provided by meteorological models, none of the existing models has been specifically designed to support short-term (e.g. 20 minutes to 2 hours), highly accurate prediction of atmospheric properties over a very small airspace volume adjacent to ground level (e.g. 25 nautical miles×15,000 ft, so much smaller than the typical volumes considered in meteorology).
Consequently, current airspace management automation tools (such as flight data processing systems, arrival management tools, flight management systems, and mission command and control environments in charge of supporting the operation of aerial vehicles) either rely on very poor atmospheric data coming from coarse and very often out-of-date observations or historic records, or they simply assume standard atmospheric conditions. As a result of this uncertainty in atmospheric conditions, there is a shortfall in the accuracy of the predicted trajectories upon which flight management and traffic management decisions are based which, in turn, leads to large inefficiencies and unacceptable performance.
There are long-term efforts to provide improved forecasts of localised atmospheric conditions. These efforts envisage developing local meteorological models that receive as inputs as many direct wind (w), pressure (p) and temperature (T) observations as possible. These observations would be provided by the air vehicles operating within the airspace volume of interest. However, such an approach will have to overcome some significant hurdles if it is to succeed, as follows.
To collect the required atmospheric data from the air vehicles requires collaboration by the air vehicles. For example, the air vehicles must be equipped with atmospheric data acquisition and communication facilities such as ADS-B. This technology requires observation equipment, data processing and datalink communication equipment. At present, such airborne equipment is barely available, remains costly and is still subject to standardization and adoption issues. It is anticipated that such a system will take many years to develop into a form that may be readily deployed.
The necessary data communication is subject to datalink issues such as range and bandwidth limitations, outages and prioritisation when demand is too heavy. To overcome these issues requires putting in place a service-oriented collaborative process and this is not trivial, bearing in mind the many parties involved, all of which have to be properly equipped.
The potentially large data set collected from the many air vehicles places a requirement for very high computing power to produce the model outputs.
In summary, there are no existing satisfactory solutions to the problem of accurately predicting atmospheric properties in a small volume of airspace adjacent to the ground, and the solutions envisaged will only be achievable in a datalink-enabled collaborative environment. In domains like air traffic management, to realise such a system is expected to take a long time, in the order of fifteen years or more, and will require overcoming significant technical, economical, political and regulatory hurdles.
The ability to describe and to predict an aircraft's trajectory is also useful, for many reasons. By trajectory, a four-dimensional description of the aircraft's path is meant. The description may be the evolution of the aircraft's state with time, where the state may include the position of the aircraft's centre of mass and other aspects of its motion such as velocity, attitude and weight. In order to predict an aircraft's trajectory unambiguously, one must solve a set of differential equations that model both aircraft behaviour and atmospheric conditions.
Aircraft intent is described using a formal language, and provides an unambiguous description of an aircraft's trajectory, i.e. the information it contains closes all degrees of freedom of the aircraft's motion. As such, it represents a complete description of the trajectory. The aircraft intent may be expressed as a structured set of instructions that are used by a trajectory computation infrastructure to calculate the resulting unique trajectory. The instructions should include configuration details of the aircraft (e.g. landing gear deployment), and procedures to be followed during maneuvers and normal flight (e.g. track a certain lateral path or hold a given airspeed). These instructions capture the basic commands and guidance modes at the disposal of the pilot and the aircraft's flight management system to direct the operation of the aircraft. Thus, aircraft intent may be thought of as an abstraction of the way in which an aircraft is commanded to behave by the pilot and/or flight management system.
EP patent application 07380259.7, published as EP-A-2,040,137, also in the name of The Boeing Company, describes aircraft intent in more detail, and the disclosure of this application is incorporated herein in its entirety by reference.
Aircraft intent data may be provided by the aircraft or the aircraft operator. However, aircraft intent data is not always readily available. In such situations, it may be useful to be able to obtain the aircraft intent in some other way.